{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from models import *\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "ss_X_dep = StandardScaler()\n",
    "ss_y_dep = StandardScaler()\n",
    "\n",
    "def rmse(y1, y2):\n",
    "    return np.sqrt(mean_squared_error(y1, y2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_FC_prediction(X, Y, X_test=None, iters=20000, learning_rate=1e-4, dropout_prob=0.5):\n",
    "    if dropout_prob > 1. or dropout_prob < 0.:\n",
    "        raise Exception('Dropout level must be in interval [0, 1]')\n",
    "    print(\"learning rate:\", learning_rate)\n",
    "    print(\"dropout:\", dropout_prob)\n",
    "    print(\"iterations:\", iters)\n",
    "    num_month = Y.shape[0]\n",
    "    input_shape = X.shape[1]\n",
    "    print('num_month:', num_month)\n",
    "    print('variable size:', input_shape)\n",
    "    model = LSTM_FC_Model(num_input=input_shape, num_hidden=[40], num_output=1)\n",
    "\n",
    "    Loss = []\n",
    "    print('Start training......')\n",
    "    for iter in range(iters + 1):\n",
    "        loss = model.fit(X, Y, learning_rate, dropout_prob)\n",
    "        Loss.append(loss)\n",
    "        if iter % 1000 == 0:\n",
    "            print(\"iteration: %s, loss: %s\" % (iter, loss))\n",
    "\n",
    "    # Saving model\n",
    "    model.save_model_params('checkpoints/LSTM_FC_CKPT')\n",
    "\n",
    "    print('Start predicting......')\n",
    "    Y_test = model.predict(X_test)\n",
    "    print('Done.')\n",
    "    return Y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and validate LSTM_FC Model in dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_dep shape (144, 5)\n",
      "y_train_dep shape (144, 1)\n",
      "X_test_dep shape (24, 5)\n",
      "X_train_dep_std shape (144, 5)\n",
      "y_train_dep_std shape (144, 1)\n",
      "X_test_dep_std shape (168, 5)\n",
      "learning rate: 0.0001\n",
      "dropout: 0.5\n",
      "iterations: 14000\n",
      "num_month: 144\n",
      "variable size: 5\n",
      "Build LSTM_FC Model......\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n",
      "WARNING (theano.tensor.blas): We did not find a dynamic library in the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training......\n",
      "iteration: 0, loss: 371.0372087489539\n",
      "iteration: 1000, loss: 134.1267558632824\n",
      "iteration: 2000, loss: 90.1222537629338\n",
      "iteration: 3000, loss: 77.63701763793665\n",
      "iteration: 4000, loss: 66.41027382549018\n",
      "iteration: 5000, loss: 51.01219022901203\n",
      "iteration: 6000, loss: 47.164085877529835\n",
      "iteration: 7000, loss: 51.9101562927477\n",
      "iteration: 8000, loss: 41.27283749506584\n",
      "iteration: 9000, loss: 43.21454358961822\n",
      "iteration: 10000, loss: 43.26276443680515\n",
      "iteration: 11000, loss: 38.1480625866977\n",
      "iteration: 12000, loss: 34.393987462582146\n",
      "iteration: 13000, loss: 31.22542563962359\n",
      "iteration: 14000, loss: 28.820937470184624\n",
      "Start predicting......\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "data = pd.read_csv('data/demo.csv')\n",
    "Inputs = data.drop('Year', axis=1).drop('Depth', axis=1)\n",
    "Outputs = data['Depth']\n",
    "\n",
    "Inputs = Inputs.as_matrix()\n",
    "Outputs = Outputs.as_matrix().reshape(-1, 1)\n",
    "\n",
    "# First 12 years of data\n",
    "X_train_dep = Inputs[0:144]\n",
    "y_train_dep = Outputs[0:144]\n",
    "\n",
    "# Last two years of data\n",
    "X_test_dep = Inputs[144:]\n",
    "\n",
    "print(\"X_train_dep shape\", X_train_dep.shape)\n",
    "print(\"y_train_dep shape\", y_train_dep.shape)\n",
    "print(\"X_test_dep shape\", X_test_dep.shape)\n",
    "\n",
    "X = np.concatenate([X_train_dep, X_test_dep], axis=0)\n",
    "\n",
    "# Standardization\n",
    "X = ss_X_dep.fit_transform(X)\n",
    "\n",
    "# First 12 years of data\n",
    "X_train_dep_std = X[0:144]\n",
    "y_train_dep_std = ss_y_dep.fit_transform(y_train_dep)\n",
    "\n",
    "# All 14 years of data\n",
    "X_test_dep_std  = X\n",
    "\n",
    "print(\"X_train_dep_std shape\", X_train_dep_std.shape)\n",
    "print(\"y_train_dep_std shape\", y_train_dep_std.shape)\n",
    "print(\"X_test_dep_std shape\", X_test_dep_std.shape)\n",
    "\n",
    "y_pred_dep = LSTM_FC_prediction(X_train_dep_std, y_train_dep_std, X_test_dep_std, iters=14000, learning_rate=1e-4)\n",
    "y_pred_dep = ss_y_dep.inverse_transform(y_pred_dep[144:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of R-squared of water table depth is  0.6699877233397822\n",
      "The value of Root mean squared error of water table depth is  0.9449295363104716\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6b6012198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('The value of R-squared of water table depth is ', r2_score(Outputs[144:], y_pred_dep))\n",
    "print('The value of Root mean squared error of water table depth is ', rmse(Outputs[144:], y_pred_dep))\n",
    "\n",
    "f, ax1 = plt.subplots(1, 1, sharex=True, figsize=(8, 6))\n",
    "\n",
    "ax1.plot(Outputs[144:], color=\"mediumblue\", linestyle=\"-\", marker=\"o\", linewidth=1.5, label=\"Measurements\")\n",
    "ax1.plot(y_pred_dep, color=\"r\", linestyle=\"-\", marker=\"s\", linewidth=1.5, label=\"Proposed model\")\n",
    "\n",
    "ax1.set_title('Results', fontsize=16, fontweight='normal')\n",
    "# ax1.spines['top'].set_visible(False)\n",
    "# ax1.spines['right'].set_visible(False)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xticks(fontsize=14,fontweight='normal')\n",
    "plt.yticks(fontsize=14,fontweight='normal')\n",
    "plt.xlabel('Time (Month)', fontsize=14)\n",
    "plt.ylabel('Water table depth (m)', fontsize=14)\n",
    "plt.xlim(0, 25)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Model parameters from checkpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build LSTM_FC Model......\n",
      "Start loading model......\n",
      "Model restored.\n",
      "Start predicting......\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "model_restore = LSTM_FC_Model(num_input=5, num_hidden=[40], num_output=1)\n",
    "# Loading model\n",
    "print('Start loading model......')\n",
    "model_restore.load_model_params('checkpoints/LSTM_FC_CKPT')\n",
    "print('Model restored.')\n",
    "print('Start predicting......')\n",
    "y_pred_dep_ = model_restore.predict(X_test_dep_std)\n",
    "y_pred_dep_ = ss_y_dep.inverse_transform(y_pred_dep_[144:])\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the value of R-squared of Evaporation is  0.6699877233397822\n",
      "the value of Root mean squared error of Evaporation is  0.9449295363104716\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe69a570400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('the value of R-squared of Evaporation is ', r2_score(Outputs[144:], y_pred_dep_))\n",
    "print('the value of Root mean squared error of Evaporation is ', rmse(Outputs[144:], y_pred_dep_))\n",
    "\n",
    "\n",
    "f, ax1 = plt.subplots(1, 1, sharex=True, figsize=(8, 6))\n",
    "\n",
    "ax1.plot(Outputs[144:], color=\"mediumblue\", linestyle=\"-\", marker=\"o\", linewidth=1.5, label=\"Measurements\")\n",
    "ax1.plot(y_pred_dep_, color=\"r\", linestyle=\"-\", marker=\"s\", linewidth=1.5, label=\"Proposed model\")\n",
    "\n",
    "ax1.set_title('Results', fontsize=16, fontweight='normal')\n",
    "# ax1.spines['top'].set_visible(False)\n",
    "# ax1.spines['right'].set_visible(False)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xticks(fontsize=14,fontweight='normal')\n",
    "plt.yticks(fontsize=14,fontweight='normal')\n",
    "plt.xlabel('Time (Month)', fontsize=14)\n",
    "plt.ylabel('Water table depth (m)', fontsize=14)\n",
    "plt.xlim(0, 25)\n",
    "\n",
    "plt.show()"
   ]
  }
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